{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from lightgbm import LGBMClassifier\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/home/luke/Desktop/kaggle/Home_Credit_Default_Risk')\n",
    "application_test = pd.read_csv('application_test.csv')\n",
    "application_train = pd.read_csv('application_train.csv')\n",
    "#bureau = pd.read_csv('bureau.csv')\n",
    "#bureau_balance = pd.read_csv('bureau_balance.csv')\n",
    "#credit_card_balance = pd.read_csv('credit_card_balance.csv')\n",
    "#installments_payments = pd.read_csv('installments_payments.csv')\n",
    "#POS_CASH_balance = pd.read_csv('POS_CASH_balance.csv')\n",
    "#previous_application = pd.read_csv('previous_application.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of application_test is (48744, 121)\n",
      "Size of application_train is (307511, 122)\n",
      "Size of bureau is (1716428, 17)\n",
      "Size of bureau_balance is (27299925, 3)\n",
      "Size of credit_card_balance is (3840312, 23)\n",
      "Size of installments_payments is (13605401, 8)\n",
      "Size of POS_CASH_balance is (10001358, 8)\n",
      "Size of previous_application is (1670214, 37)\n"
     ]
    }
   ],
   "source": [
    "for names in ['application_test','application_train','bureau','bureau_balance','credit_card_balance','installments_payments','POS_CASH_balance','previous_application']:\n",
    "    print('Size of', names,'is',eval(names).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        NaN   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         0.0   \n",
       "3                       NaN                         NaN   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "3                         NaN  \n",
       "4                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',\n",
       "       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN',\n",
       "       'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY', 'AMT_GOODS_PRICE',\n",
       "       'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE',\n",
       "       'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE',\n",
       "       'REGION_POPULATION_RELATIVE', 'DAYS_BIRTH', 'DAYS_EMPLOYED',\n",
       "       'DAYS_REGISTRATION', 'DAYS_ID_PUBLISH', 'OWN_CAR_AGE',\n",
       "       'FLAG_MOBIL', 'FLAG_EMP_PHONE', 'FLAG_WORK_PHONE',\n",
       "       'FLAG_CONT_MOBILE', 'FLAG_PHONE', 'FLAG_EMAIL', 'OCCUPATION_TYPE',\n",
       "       'CNT_FAM_MEMBERS', 'REGION_RATING_CLIENT',\n",
       "       'REGION_RATING_CLIENT_W_CITY', 'WEEKDAY_APPR_PROCESS_START',\n",
       "       'HOUR_APPR_PROCESS_START', 'REG_REGION_NOT_LIVE_REGION',\n",
       "       'REG_REGION_NOT_WORK_REGION', 'LIVE_REGION_NOT_WORK_REGION',\n",
       "       'REG_CITY_NOT_LIVE_CITY', 'REG_CITY_NOT_WORK_CITY',\n",
       "       'LIVE_CITY_NOT_WORK_CITY', 'ORGANIZATION_TYPE', 'EXT_SOURCE_1',\n",
       "       'EXT_SOURCE_2', 'EXT_SOURCE_3', 'APARTMENTS_AVG',\n",
       "       'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG',\n",
       "       'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG',\n",
       "       'ENTRANCES_AVG', 'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG',\n",
       "       'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG',\n",
       "       'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAREA_AVG', 'APARTMENTS_MODE',\n",
       "       'BASEMENTAREA_MODE', 'YEARS_BEGINEXPLUATATION_MODE',\n",
       "       'YEARS_BUILD_MODE', 'COMMONAREA_MODE', 'ELEVATORS_MODE',\n",
       "       'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE',\n",
       "       'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE',\n",
       "       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE',\n",
       "       'APARTMENTS_MEDI', 'BASEMENTAREA_MEDI',\n",
       "       'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI',\n",
       "       'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI',\n",
       "       'FLOORSMAX_MEDI', 'FLOORSMIN_MEDI', 'LANDAREA_MEDI',\n",
       "       'LIVINGAPARTMENTS_MEDI', 'LIVINGAREA_MEDI',\n",
       "       'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI',\n",
       "       'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'TOTALAREA_MODE',\n",
       "       'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE',\n",
       "       'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE',\n",
       "       'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE',\n",
       "       'DAYS_LAST_PHONE_CHANGE', 'FLAG_DOCUMENT_2', 'FLAG_DOCUMENT_3',\n",
       "       'FLAG_DOCUMENT_4', 'FLAG_DOCUMENT_5', 'FLAG_DOCUMENT_6',\n",
       "       'FLAG_DOCUMENT_7', 'FLAG_DOCUMENT_8', 'FLAG_DOCUMENT_9',\n",
       "       'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_11', 'FLAG_DOCUMENT_12',\n",
       "       'FLAG_DOCUMENT_13', 'FLAG_DOCUMENT_14', 'FLAG_DOCUMENT_15',\n",
       "       'FLAG_DOCUMENT_16', 'FLAG_DOCUMENT_17', 'FLAG_DOCUMENT_18',\n",
       "       'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',\n",
       "       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       "       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       "       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    282686\n",
       "1     24825\n",
       "Name: TARGET, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.TARGET.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8488b31ac8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "application_train.TARGET.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values_tables(df):\n",
    "    \n",
    "    mis_val = df.isnull().sum()\n",
    "    mis_val_percent = 100*df.isnull().sum()/len(df)\n",
    "    mis_val_table = pd.concat([mis_val,mis_val_percent],axis = 1)\n",
    "    \n",
    "    mis_val_table_ren_columns = mis_val_table.rename(\n",
    "    columns = {0:'Missing Values',1:'% of Total Values'})\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "        mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "    '% of Total Values',ascending=False).round(1)\n",
    "    \n",
    "    print('Your selected dataframe has '+str(df.shape[1])+\" columns.\\n\" \"There are \" + str(mis_val_table_ren_columns.shape[0])+\n",
    "         ' column that have missing values.')\n",
    "    return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 122 columns.\n",
      "There are 67 column that have missing values.\n"
     ]
    }
   ],
   "source": [
    "missing_values = missing_values_tables(application_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>214865</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>213514</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>210295</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>210199</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <td>208642</td>\n",
       "      <td>67.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <td>204488</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <td>202929</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <td>182590</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>179943</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>173378</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>169682</td>\n",
       "      <td>55.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <td>163891</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <td>154350</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <td>154297</td>\n",
       "      <td>50.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <td>153020</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <td>150007</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <td>148431</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "      <td>145755</td>\n",
       "      <td>47.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <td>96391</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>60965</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <td>41519</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_TYPE_SUITE</th>\n",
       "      <td>1292</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>660</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <td>278</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Missing Values  % of Total Values\n",
       "COMMONAREA_MEDI                       214865               69.9\n",
       "COMMONAREA_AVG                        214865               69.9\n",
       "COMMONAREA_MODE                       214865               69.9\n",
       "NONLIVINGAPARTMENTS_MEDI              213514               69.4\n",
       "NONLIVINGAPARTMENTS_MODE              213514               69.4\n",
       "NONLIVINGAPARTMENTS_AVG               213514               69.4\n",
       "FONDKAPREMONT_MODE                    210295               68.4\n",
       "LIVINGAPARTMENTS_MODE                 210199               68.4\n",
       "LIVINGAPARTMENTS_MEDI                 210199               68.4\n",
       "LIVINGAPARTMENTS_AVG                  210199               68.4\n",
       "FLOORSMIN_MODE                        208642               67.8\n",
       "FLOORSMIN_MEDI                        208642               67.8\n",
       "FLOORSMIN_AVG                         208642               67.8\n",
       "YEARS_BUILD_MODE                      204488               66.5\n",
       "YEARS_BUILD_MEDI                      204488               66.5\n",
       "YEARS_BUILD_AVG                       204488               66.5\n",
       "OWN_CAR_AGE                           202929               66.0\n",
       "LANDAREA_AVG                          182590               59.4\n",
       "LANDAREA_MEDI                         182590               59.4\n",
       "LANDAREA_MODE                         182590               59.4\n",
       "BASEMENTAREA_MEDI                     179943               58.5\n",
       "BASEMENTAREA_AVG                      179943               58.5\n",
       "BASEMENTAREA_MODE                     179943               58.5\n",
       "EXT_SOURCE_1                          173378               56.4\n",
       "NONLIVINGAREA_MEDI                    169682               55.2\n",
       "NONLIVINGAREA_MODE                    169682               55.2\n",
       "NONLIVINGAREA_AVG                     169682               55.2\n",
       "ELEVATORS_MEDI                        163891               53.3\n",
       "ELEVATORS_MODE                        163891               53.3\n",
       "ELEVATORS_AVG                         163891               53.3\n",
       "...                                      ...                ...\n",
       "LIVINGAREA_MEDI                       154350               50.2\n",
       "LIVINGAREA_MODE                       154350               50.2\n",
       "LIVINGAREA_AVG                        154350               50.2\n",
       "HOUSETYPE_MODE                        154297               50.2\n",
       "FLOORSMAX_MEDI                        153020               49.8\n",
       "FLOORSMAX_AVG                         153020               49.8\n",
       "FLOORSMAX_MODE                        153020               49.8\n",
       "YEARS_BEGINEXPLUATATION_AVG           150007               48.8\n",
       "YEARS_BEGINEXPLUATATION_MEDI          150007               48.8\n",
       "YEARS_BEGINEXPLUATATION_MODE          150007               48.8\n",
       "TOTALAREA_MODE                        148431               48.3\n",
       "EMERGENCYSTATE_MODE                   145755               47.4\n",
       "OCCUPATION_TYPE                        96391               31.3\n",
       "EXT_SOURCE_3                           60965               19.8\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK             41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_DAY              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_MON              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_QRT              41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR             41519               13.5\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR             41519               13.5\n",
       "NAME_TYPE_SUITE                         1292                0.4\n",
       "DEF_30_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "OBS_60_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "DEF_60_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "OBS_30_CNT_SOCIAL_CIRCLE                1021                0.3\n",
       "EXT_SOURCE_2                             660                0.2\n",
       "AMT_GOODS_PRICE                          278                0.1\n",
       "AMT_ANNUITY                               12                0.0\n",
       "CNT_FAM_MEMBERS                            2                0.0\n",
       "DAYS_LAST_PHONE_CHANGE                     1                0.0\n",
       "\n",
       "[67 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    65\n",
       "int64      41\n",
       "object     16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE             2\n",
       "CODE_GENDER                    3\n",
       "FLAG_OWN_CAR                   2\n",
       "FLAG_OWN_REALTY                2\n",
       "NAME_TYPE_SUITE                7\n",
       "NAME_INCOME_TYPE               8\n",
       "NAME_EDUCATION_TYPE            5\n",
       "NAME_FAMILY_STATUS             6\n",
       "NAME_HOUSING_TYPE              6\n",
       "OCCUPATION_TYPE               18\n",
       "WEEKDAY_APPR_PROCESS_START     7\n",
       "ORGANIZATION_TYPE             58\n",
       "FONDKAPREMONT_MODE             4\n",
       "HOUSETYPE_MODE                 3\n",
       "WALLSMATERIAL_MODE             7\n",
       "EMERGENCYSTATE_MODE            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.select_dtypes('object').apply(pd.Series.nunique,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 columns were label encoded.\n"
     ]
    }
   ],
   "source": [
    "le  = LabelEncoder()\n",
    "le_count = 0\n",
    "\n",
    "for col in application_train:\n",
    "    if application_train[col].dtype == 'object':\n",
    "        if len(list(application_train[col].unique())) <= 2:\n",
    "            le.fit(application_train[col])\n",
    "            application_train[col] = le.transform(application_train[col])\n",
    "            application_test[col] = le.transform(application_test[col])\n",
    "            \n",
    "            le_count += 1\n",
    "print('%d columns were label encoded.' % le_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "application_train = pd.get_dummies(application_train)\n",
    "application_test = pd.get_dummies(application_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = application_train['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Features Shape:  (307511, 239)\n",
      "Testing Features Shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "application_train,application_test = application_train.align(application_test,join='inner',axis = 1)\n",
    "\n",
    "print('Training Features Shape: ', application_train.shape)\n",
    "print('Testing Features Shape: ', application_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Positive Correlations: \n",
      " DAYS_REGISTRATION                                    0.041975\n",
      "OCCUPATION_TYPE_Laborers                             0.043019\n",
      "FLAG_DOCUMENT_3                                      0.044346\n",
      "REG_CITY_NOT_LIVE_CITY                               0.044395\n",
      "FLAG_EMP_PHONE                                       0.045982\n",
      "NAME_EDUCATION_TYPE_Secondary / secondary special    0.049824\n",
      "REG_CITY_NOT_WORK_CITY                               0.050994\n",
      "DAYS_ID_PUBLISH                                      0.051457\n",
      "CODE_GENDER_M                                        0.054713\n",
      "DAYS_LAST_PHONE_CHANGE                               0.055218\n",
      "NAME_INCOME_TYPE_Working                             0.057481\n",
      "REGION_RATING_CLIENT                                 0.058899\n",
      "REGION_RATING_CLIENT_W_CITY                          0.060893\n",
      "DAYS_BIRTH                                           0.078239\n",
      "TARGET                                               1.000000\n",
      "Name: TARGET, dtype: float64\n",
      "\n",
      "Most Negative Correlations :\n",
      " EXT_SOURCE_3                           -0.178919\n",
      "EXT_SOURCE_2                           -0.160472\n",
      "EXT_SOURCE_1                           -0.155317\n",
      "NAME_EDUCATION_TYPE_Higher education   -0.056593\n",
      "CODE_GENDER_F                          -0.054704\n",
      "NAME_INCOME_TYPE_Pensioner             -0.046209\n",
      "ORGANIZATION_TYPE_XNA                  -0.045987\n",
      "DAYS_EMPLOYED                          -0.044932\n",
      "FLOORSMAX_AVG                          -0.044003\n",
      "FLOORSMAX_MEDI                         -0.043768\n",
      "FLOORSMAX_MODE                         -0.043226\n",
      "EMERGENCYSTATE_MODE_No                 -0.042201\n",
      "HOUSETYPE_MODE_block of flats          -0.040594\n",
      "AMT_GOODS_PRICE                        -0.039645\n",
      "REGION_POPULATION_RELATIVE             -0.037227\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "application_train['TARGET'] = train_labels\n",
    "correlations = application_train.corr()['TARGET'].sort_values()\n",
    "\n",
    "print('Most Positive Correlations: \\n', correlations.tail(15))\n",
    "print('\\nMost Negative Correlations :\\n', correlations.head(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07823930830982712"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train['DAYS_BIRTH'] = abs(application_train['DAYS_BIRTH'])\n",
    "application_train['DAYS_BIRTH'].corr(application_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.hist(application_train['DAYS_BIRTH']/365,edgecolor='k',bins=25)\n",
    "plt.title('Age of Client'); plt.xlabel('Age(years)'); plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of Ages')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.kdeplot(application_train.loc[application_train['TARGET']==0,'DAYS_BIRTH']/365,label='target == 0')\n",
    "\n",
    "sns.kdeplot(application_train.loc[application_train['TARGET']==1,'DAYS_BIRTH']/365,label = 'target==1')\n",
    "\n",
    "plt.xlabel('Age(years)');plt.ylabel('Density');plt.title('Distribution of Ages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luke/anaconda3/envs/tf/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/home/luke/anaconda3/envs/tf/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9461</td>\n",
       "      <td>25.920548</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16765</td>\n",
       "      <td>45.931507</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19046</td>\n",
       "      <td>52.180822</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>19005</td>\n",
       "      <td>52.068493</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19932</td>\n",
       "      <td>54.608219</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>16941</td>\n",
       "      <td>46.413699</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>13778</td>\n",
       "      <td>37.747945</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>18850</td>\n",
       "      <td>51.643836</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>20099</td>\n",
       "      <td>55.065753</td>\n",
       "      <td>(55.0, 60.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>14469</td>\n",
       "      <td>39.641096</td>\n",
       "      <td>(35.0, 40.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  DAYS_BIRTH  YEARS_BIRTH  YEARS_BINNED\n",
       "0       1        9461    25.920548  (25.0, 30.0]\n",
       "1       0       16765    45.931507  (45.0, 50.0]\n",
       "2       0       19046    52.180822  (50.0, 55.0]\n",
       "3       0       19005    52.068493  (50.0, 55.0]\n",
       "4       0       19932    54.608219  (50.0, 55.0]\n",
       "5       0       16941    46.413699  (45.0, 50.0]\n",
       "6       0       13778    37.747945  (35.0, 40.0]\n",
       "7       0       18850    51.643836  (50.0, 55.0]\n",
       "8       0       20099    55.065753  (55.0, 60.0]\n",
       "9       0       14469    39.641096  (35.0, 40.0]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_data = application_train[['TARGET','DAYS_BIRTH']]\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH']/365\n",
    "\n",
    "age_data['YEARS_BINNED']= pd.cut(age_data['YEARS_BIRTH'], bins = np.linspace(20,70,num = 11))\n",
    "age_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.123036</td>\n",
       "      <td>8532.795625</td>\n",
       "      <td>23.377522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>10155.219250</td>\n",
       "      <td>27.822518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.102814</td>\n",
       "      <td>11854.848377</td>\n",
       "      <td>32.479037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.089414</td>\n",
       "      <td>13707.908253</td>\n",
       "      <td>37.555913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.078491</td>\n",
       "      <td>15497.661233</td>\n",
       "      <td>42.459346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.074171</td>\n",
       "      <td>17323.900441</td>\n",
       "      <td>47.462741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.066968</td>\n",
       "      <td>19196.494791</td>\n",
       "      <td>52.593136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.055314</td>\n",
       "      <td>20984.262742</td>\n",
       "      <td>57.491131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.052737</td>\n",
       "      <td>22780.547460</td>\n",
       "      <td>62.412459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.037270</td>\n",
       "      <td>24292.614340</td>\n",
       "      <td>66.555108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET    DAYS_BIRTH  YEARS_BIRTH\n",
       "YEARS_BINNED                                     \n",
       "(20.0, 25.0]  0.123036   8532.795625    23.377522\n",
       "(25.0, 30.0]  0.111436  10155.219250    27.822518\n",
       "(30.0, 35.0]  0.102814  11854.848377    32.479037\n",
       "(35.0, 40.0]  0.089414  13707.908253    37.555913\n",
       "(40.0, 45.0]  0.078491  15497.661233    42.459346\n",
       "(45.0, 50.0]  0.074171  17323.900441    47.462741\n",
       "(50.0, 55.0]  0.066968  19196.494791    52.593136\n",
       "(55.0, 60.0]  0.055314  20984.262742    57.491131\n",
       "(60.0, 65.0]  0.052737  22780.547460    62.412459\n",
       "(65.0, 70.0]  0.037270  24292.614340    66.555108"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# Graph the age bins and the average of the target as a bar plot\n",
    "plt.bar(age_groups.index.astype(str), 100 * age_groups['TARGET'])\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155317</td>\n",
       "      <td>-0.160472</td>\n",
       "      <td>-0.178919</td>\n",
       "      <td>0.078239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <td>-0.155317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>-0.600610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <td>-0.160472</td>\n",
       "      <td>0.213982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>-0.091996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXT_SOURCE_3</th>\n",
       "      <td>-0.178919</td>\n",
       "      <td>0.186846</td>\n",
       "      <td>0.109167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.205478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <td>0.078239</td>\n",
       "      <td>-0.600610</td>\n",
       "      <td>-0.091996</td>\n",
       "      <td>-0.205478</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET  EXT_SOURCE_1  EXT_SOURCE_2  EXT_SOURCE_3  DAYS_BIRTH\n",
       "TARGET        1.000000     -0.155317     -0.160472     -0.178919    0.078239\n",
       "EXT_SOURCE_1 -0.155317      1.000000      0.213982      0.186846   -0.600610\n",
       "EXT_SOURCE_2 -0.160472      0.213982      1.000000      0.109167   -0.091996\n",
       "EXT_SOURCE_3 -0.178919      0.186846      0.109167      1.000000   -0.205478\n",
       "DAYS_BIRTH    0.078239     -0.600610     -0.091996     -0.205478    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ext_data = application_train[['TARGET', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "ext_data_corrs = ext_data.corr()\n",
    "ext_data_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "\n",
    "# Heatmap of correlations\n",
    "sns.heatmap(ext_data_corrs, cmap = plt.cm.RdYlBu_r, vmin = -0.25, annot = True, vmax = 0.6)\n",
    "plt.title('Correlation Heatmap');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a new dataframe for polynomial features\n",
    "poly_features = application_train[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH', 'TARGET']]\n",
    "poly_features_test = application_test[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH']]\n",
    "\n",
    "# imputer for handling missing values\n",
    "from sklearn.preprocessing import Imputer\n",
    "imputer = Imputer(strategy = 'median')\n",
    "\n",
    "poly_target = poly_features['TARGET']\n",
    "\n",
    "poly_features = poly_features.drop(columns = ['TARGET'])\n",
    "\n",
    "# Need to impute missing values\n",
    "poly_features = imputer.fit_transform(poly_features)\n",
    "poly_features_test = imputer.transform(poly_features_test)\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "                                  \n",
    "# Create the polynomial object with specified degree\n",
    "poly_transformer = PolynomialFeatures(degree = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial Features shape:  (307511, 35)\n"
     ]
    }
   ],
   "source": [
    "poly_transformer.fit(poly_features)\n",
    "\n",
    "# Transform the features\n",
    "poly_features = poly_transformer.transform(poly_features)\n",
    "poly_features_test = poly_transformer.transform(poly_features_test)\n",
    "print('Polynomial Features shape: ', poly_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1',\n",
       " 'EXT_SOURCE_1',\n",
       " 'EXT_SOURCE_2',\n",
       " 'EXT_SOURCE_3',\n",
       " 'DAYS_BIRTH',\n",
       " 'EXT_SOURCE_1^2',\n",
       " 'EXT_SOURCE_1 EXT_SOURCE_2',\n",
       " 'EXT_SOURCE_1 EXT_SOURCE_3',\n",
       " 'EXT_SOURCE_1 DAYS_BIRTH',\n",
       " 'EXT_SOURCE_2^2',\n",
       " 'EXT_SOURCE_2 EXT_SOURCE_3',\n",
       " 'EXT_SOURCE_2 DAYS_BIRTH',\n",
       " 'EXT_SOURCE_3^2',\n",
       " 'EXT_SOURCE_3 DAYS_BIRTH',\n",
       " 'DAYS_BIRTH^2']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_transformer.get_feature_names(input_features = ['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 'DAYS_BIRTH'])[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXT_SOURCE_2 EXT_SOURCE_3                -0.193939\n",
      "EXT_SOURCE_1 EXT_SOURCE_2 EXT_SOURCE_3   -0.189605\n",
      "EXT_SOURCE_2^2 EXT_SOURCE_3              -0.176428\n",
      "EXT_SOURCE_2 EXT_SOURCE_3^2              -0.172282\n",
      "EXT_SOURCE_1 EXT_SOURCE_2                -0.166625\n",
      "EXT_SOURCE_1 EXT_SOURCE_3                -0.164065\n",
      "EXT_SOURCE_2                             -0.160295\n",
      "EXT_SOURCE_1 EXT_SOURCE_2^2              -0.156867\n",
      "EXT_SOURCE_3                             -0.155892\n",
      "EXT_SOURCE_1 EXT_SOURCE_3^2              -0.150822\n",
      "Name: TARGET, dtype: float64\n",
      "EXT_SOURCE_1 EXT_SOURCE_2 DAYS_BIRTH    0.155891\n",
      "EXT_SOURCE_2 DAYS_BIRTH                 0.156873\n",
      "EXT_SOURCE_2 EXT_SOURCE_3 DAYS_BIRTH    0.181283\n",
      "TARGET                                  1.000000\n",
      "1                                            NaN\n",
      "Name: TARGET, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a dataframe of the features \n",
    "poly_features = pd.DataFrame(poly_features, \n",
    "                             columns = poly_transformer.get_feature_names(['EXT_SOURCE_1', 'EXT_SOURCE_2', \n",
    "                                                                           'EXT_SOURCE_3', 'DAYS_BIRTH']))\n",
    "\n",
    "# Add in the target\n",
    "poly_features['TARGET'] = poly_target\n",
    "\n",
    "# Find the correlations with the target\n",
    "poly_corrs = poly_features.corr()['TARGET'].sort_values()\n",
    "\n",
    "# Display most negative and most positive\n",
    "print(poly_corrs.head(10))\n",
    "print(poly_corrs.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data with polynomial features shape:  (307511, 274)\n",
      "Testing data with polynomial features shape:   (48744, 274)\n"
     ]
    }
   ],
   "source": [
    "# Put test features into dataframe\n",
    "poly_features_test = pd.DataFrame(poly_features_test, \n",
    "                                  columns = poly_transformer.get_feature_names(['EXT_SOURCE_1', 'EXT_SOURCE_2', \n",
    "                                                                                'EXT_SOURCE_3', 'DAYS_BIRTH']))\n",
    "\n",
    "# Merge polynomial features into training dataframe\n",
    "poly_features['SK_ID_CURR'] = application_train['SK_ID_CURR']\n",
    "app_train_poly = application_train.merge(poly_features, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Merge polnomial features into testing dataframe\n",
    "poly_features_test['SK_ID_CURR'] = application_test['SK_ID_CURR']\n",
    "app_test_poly = application_test.merge(poly_features_test, on = 'SK_ID_CURR', how = 'left')\n",
    "\n",
    "# Align the dataframes\n",
    "app_train_poly, app_test_poly = app_train_poly.align(app_test_poly, join = 'inner', axis = 1)\n",
    "\n",
    "# Print out the new shapes\n",
    "print('Training data with polynomial features shape: ', app_train_poly.shape)\n",
    "print('Testing data with polynomial features shape:  ', app_test_poly.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (307511, 239)\n",
      "Testing data shape:  (48744, 239)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, Imputer\n",
    "\n",
    "# Drop the target from the training data\n",
    "if 'TARGET' in application_train:\n",
    "    train = application_train.drop(columns = ['TARGET'])\n",
    "else:\n",
    "    train = application_train.copy()\n",
    "features = list(train.columns)\n",
    "\n",
    "# Copy of the testing data\n",
    "test = application_test.copy()\n",
    "\n",
    "# Median imputation of missing values\n",
    "imputer = Imputer(strategy = 'median')\n",
    "\n",
    "# Scale each feature to 0-1\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "\n",
    "# Fit on the training data\n",
    "imputer.fit(train)\n",
    "\n",
    "# Transform both training and testing data\n",
    "train = imputer.transform(train)\n",
    "test = imputer.transform(application_test)\n",
    "\n",
    "# Repeat with the scaler\n",
    "scaler.fit(train)\n",
    "train = scaler.transform(train)\n",
    "test = scaler.transform(test)\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Testing data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.0001, class_weight=None, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Make the model with the specified regularization parameter\n",
    "log_reg = LogisticRegression(C = 0.0001)\n",
    "\n",
    "# Train on the training data\n",
    "log_reg.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg_pred = log_reg.predict_proba(test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luke/anaconda3/envs/tf/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>0.087895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>0.163376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>0.110105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100028</td>\n",
       "      <td>0.076499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100038</td>\n",
       "      <td>0.155235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR    TARGET\n",
       "0      100001  0.087895\n",
       "1      100005  0.163376\n",
       "2      100013  0.110105\n",
       "3      100028  0.076499\n",
       "4      100038  0.155235"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = log_reg_pred\n",
    "\n",
    "submit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('log_reg_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 AUC : 0.755236\n",
      "Fold 2 AUC : 0.757236\n",
      "Fold 3 AUC : 0.762423\n",
      "Fold 4 AUC : 0.757265\n",
      "Fold 5 AUC : 0.758051\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luke/anaconda3/envs/tf/lib/python3.6/site-packages/ipykernel_launcher.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Format the training and testing data \n",
    "train = np.array(application_train.drop(columns = 'TARGET'))\n",
    "test = np.array(application_test)\n",
    "\n",
    "train_labels = np.array(train_labels).reshape((-1, ))\n",
    "\n",
    "# 10 fold cross validation\n",
    "folds = KFold(n_splits=5, shuffle=True, random_state=50)\n",
    "\n",
    "# Validation and test predictions\n",
    "valid_preds = np.zeros(train.shape[0])\n",
    "test_preds = np.zeros(test.shape[0])\n",
    "\n",
    "# Iterate through each fold\n",
    "for n_fold, (train_indices, valid_indices) in enumerate(folds.split(train)):\n",
    "    # Training data for the fold\n",
    "    train_fold, train_fold_labels = train[train_indices, :], train_labels[train_indices]\n",
    "    \n",
    "    # Validation data for the fold\n",
    "    valid_fold, valid_fold_labels = train[valid_indices, :], train_labels[valid_indices]\n",
    "    \n",
    "    # LightGBM classifier with hyperparameters\n",
    "    clf = LGBMClassifier(\n",
    "        n_estimators=10000,\n",
    "        learning_rate=0.1,\n",
    "        subsample=.8,\n",
    "        max_depth=7,\n",
    "        reg_alpha=.1,\n",
    "        reg_lambda=.1,\n",
    "        min_split_gain=.01,\n",
    "        min_child_weight=2\n",
    "    )\n",
    "    \n",
    "    # Fit on the training data, evaluate on the validation data\n",
    "    clf.fit(train_fold, train_fold_labels, \n",
    "            eval_set= [(train_fold, train_fold_labels), (valid_fold, valid_fold_labels)], \n",
    "            eval_metric='auc', early_stopping_rounds=100, verbose = False\n",
    "           )\n",
    "    \n",
    "    # Validation preditions\n",
    "    valid_preds[valid_indices] = clf.predict_proba(valid_fold, num_iteration=clf.best_iteration_)[:, 1]\n",
    "    \n",
    "    # Testing predictions\n",
    "    test_preds += clf.predict_proba(test, num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits\n",
    "    \n",
    "    # Display the performance for the current fold\n",
    "    print('Fold %d AUC : %0.6f' % (n_fold + 1, roc_auc_score(valid_fold_labels, valid_preds[valid_indices])))\n",
    "    \n",
    "    # Delete variables to free up memory\n",
    "    del clf, train_fold, train_fold_labels, valid_fold, valid_fold_labels\n",
    "    gc.collect()\n",
    "    \n",
    "\n",
    "# Make a submission dataframe\n",
    "submission = application_test[['SK_ID_CURR']]\n",
    "submission['TARGET'] = test_preds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"light_gbm_baseline.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Make the random forest classifier\n",
    "random_forest = RandomForestClassifier(n_estimators = 100, random_state = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train on the training data\n",
    "random_forest.fit(train, train_labels)\n",
    "\n",
    "# Make predictions on the test data\n",
    "predictions = random_forest.predict_proba(test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/luke/anaconda3/envs/tf/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Make a submission dataframe\n",
    "submit = application_test[['SK_ID_CURR']]\n",
    "submit['TARGET'] = predictions\n",
    "\n",
    "# Save the submission dataframe\n",
    "submit.to_csv('random_forest_baseline.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate and show feature importances\n",
    "def show_feature_importances(model, features):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    # Dataframe of feature importances sorted from most to least\n",
    "    results = pd.DataFrame({'feature': features, 'importance': model.feature_importances_})\n",
    "    results = results.sort_values('importance', ascending = False)\n",
    "    \n",
    "    # Display\n",
    "    print(results.head(10))\n",
    "    print('\\nNumber of features with importance greater than 0.01 = ', np.sum(results['importance'] > 0.01))\n",
    "    \n",
    "    # Plot of feature importances as horizontal bar chart\n",
    "    results.head(20).plot(x = 'feature', y = 'importance', kind = 'barh',\n",
    "                     color = 'red', edgecolor = 'k', title = 'Feature Importances');\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   feature  importance\n",
      "32            EXT_SOURCE_2    0.050268\n",
      "33            EXT_SOURCE_3    0.046797\n",
      "10              DAYS_BIRTH    0.032366\n",
      "13         DAYS_ID_PUBLISH    0.032131\n",
      "12       DAYS_REGISTRATION    0.031494\n",
      "0               SK_ID_CURR    0.030535\n",
      "7              AMT_ANNUITY    0.028903\n",
      "81  DAYS_LAST_PHONE_CHANGE    0.028474\n",
      "11           DAYS_EMPLOYED    0.027869\n",
      "6               AMT_CREDIT    0.027407\n",
      "\n",
      "Number of features with importance greater than 0.01 =  19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = show_feature_importances(random_forest, features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(application_train['TARGET'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = train[:67511]\n",
    "partial_x_train = train[67511:]\n",
    "y_val = y_train[:67511]\n",
    "partial_y_train = y_train[67511:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.21974552, 0.        , 1.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.21975113, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.21975675, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.99999439, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.99999719, 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [1.        , 0.        , 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partial_x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('x_test.csv',test,delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 240)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\"partial_y_train.csv\", partial_y_train, delimiter=\",\")\n",
    "np.savetxt(\"partial_x_train.csv\", partial_x_train, delimiter=\",\")\n",
    "\n",
    "np.savetxt(\"x_val.csv\", x_val, delimiter=\",\")\n",
    "np.savetxt(\"y_val.csv\", y_val, delimiter=\",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation='relu', input_shape=(239,)))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-05331dd99cbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartial_x_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpartial_y_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,partial_y_train,epochs = 10,batch_size=300,validation_data = (x_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import losses\n",
    "from keras import metrics\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
    "loss=losses.binary_crossentropy,\n",
    "metrics=[metrics.binary_accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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